基于自适应池化注意力Transformer的唇语识别方法

姚云; 胡振虓; 邓涛; 王晓

doi:10.11959/j.issn.2096-6652.202515

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基于自适应池化注意力Transformer的唇语识别方法

学术论文 | 更新时间：2025-07-21

- 基于自适应池化注意力Transformer的唇语识别方法
- A lip reading method based on adaptive pooling attention Transformer
- 智能科学与技术学报 2025年7卷第2期页码：211-220
- 作者机构：
  
  安徽大学人工智能学院，安徽合肥 230031
- 作者简介：
  
  [ "姚云（1996- ），男，安徽大学人工智能学院硕士生，主要研究方向为唇语识别、生成式人工智能、计算机视觉。" ]
  [ "胡振虓（2000- ），男，安徽大学人工智能学院硕士生，主要研究方向为异常行为识别、计算机视觉、深度学习。" ]
  [ "邓涛（2003- ），男，安徽大学人工智能学院本科生，主要研究方向为计算机视觉、自动驾驶、智能自主系统。" ]
  [ "王晓（1988- ），女，安徽大学人工智能学院教授，主要研究方向为社会计算、群体行为建模、无人自主系统及其平行测试。" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202515
  中图分类号： TP391.4
- 收稿：2025-02-24，
  
  修回：2025-04-28，
  
  纸质出版：2025-06-15
- 稿件说明：
移动端阅览
姚云,胡振虓,邓涛等.基于自适应池化注意力Transformer的唇语识别方法[J].智能科学与技术学报,2025,07(02):211-220.

YAO Yun,HU Zhenxiao,DENG Tao,et al.A lip reading method based on adaptive pooling attention Transformer[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):211-220.
姚云,胡振虓,邓涛等.基于自适应池化注意力Transformer的唇语识别方法[J].智能科学与技术学报,2025,07(02):211-220. DOI： 10.11959/j.issn.2096-6652.202515.

YAO Yun,HU Zhenxiao,DENG Tao,et al.A lip reading method based on adaptive pooling attention Transformer[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):211-220. DOI： 10.11959/j.issn.2096-6652.202515.

摘要

唇语识别技术通过分析一系列连续的唇部图像，建立唇部动作特征与特定语言文字之间的映射关系，实现语义信息的识别。现有方法主要依赖循环神经网络对时序视频帧进行时序特征建模，但存在显著的信息丢失问题，尤其是在视频信息不完整或存在噪声干扰时，模型往往会在区分不同时间点的唇语动作时发生混淆，导致识别精度显著下降。针对这一问题，提出基于自适应池化注意力Transformer的唇语识别方法（lip reading method based on adaptive pooling attention Transformer，APAT-LR）。该方法在标准Transformer的多头自注意力（multi-head self-attention，MHSA）机制之前，采用最大池化和平均池化的拼接策略，引入自适应池化模块，有效抑制无关信息，增强关键特征的表达，从而提升时序特征的建模能力。实验结果表明，APAT-LR在CMLR和GRID数据集上分别取得28.4%和1.9%的错误率，相较于现有方法都降低了错误率，验证了其在唇语识别任务中的有效性。

Abstract

Lip reading technology establishes the mapping relationship between lip movements and specific language characters by processing a series of consecutive lip images

thereby enabling semantic information recognition. Existing methods mainly use recurrent networks for spatiotemporal modeling of sequential video frames. However

they suffer from significant information loss

especially when the video information is incomplete or contains noise. In such cases

the model often struggles to distinguish between lip movements at different time points

leading to a significant decline in recognition performance. To address this issue

a lip reading method based on adaptive pooling attention transformer (APAT-LR) was proposed. This method introduced an adaptive pooling module before the multi-head self-attention (MHSA) mechanism in the standard Transformer

using a concatenation strategy of max pooling and average pooling. This module effectively suppressed irrelevant information and enhances the representation of key features. Experiments on the CMLR and GRID datasets showed that the proposed APAT-LR method could reduce the recognition error rate

thus verifying the effectiveness of the proposed method.

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references

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